Iterative image reconstruction that includes a total variation regularization for radial MRI

Abstract

This paper presents an iterative image reconstruction method for radial encodings in MRI based on a total variation (TV) regularization. The algebraic reconstruction method combined with total variation regularization (ART_TV) is implemented with a regularization parameter specifying the weight of the TV term in the optimization process. We used numerical simulations of a Shepp-Logan phantom, as well as experimental imaging of a phantom that included a rectangular-wave chart, to evaluate the performance of ART_TV, and to compare it with that of the Fourier transform (FT) method. The trade-off between spatial resolution and signal-to-noise ratio (SNR) was investigated for different values of the regularization parameter by experiments on a phantom and a commercially available MRI system. ART_TV was inferior to the FT with respect to the evaluation of the modulation transfer function (MTF), especially at high frequencies; however, it outperformed the FT with regard to the SNR. In accordance with the results of SNR measurement, visual impression suggested that the image quality of ART_TV was better than that of the FT for reconstruction of a noisy image of a kiwi fruit. In conclusion, ART_TV provides radial MRI with improved image quality for low-SNR data; however, the regularization parameter in ART_TV is a critical factor for obtaining improvement over the FT.

VTDCE‐Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data

“…16,17 ) validated direct estimation methods for higher undersampling rate of 100× and gave the flexibility to incorporate any prior. Experiments with the breast data 13 showed that at low undersampling rates, DLbased indirect methods perform significantly better; however, at higher undersampling rate, the iterative direct techniques using total variation (TV) 19 regularization perform significantly better. However, direct iterative techniques are computationally expensive and require manual tuning of hyper parameters.…”

ART-TV Algorithm for Diffuse Correlation Tomography Blood Flow Imaging

“…Hence, an iterative CT reconstruction algorithm with Edge-Preserving TV (EPTV) regularization was developed to reconstruct CT images from highly under-sampled data obtained with few-view and limited-angle data [23,[30][31]. In the unfully-collected k-space Magnetic Resonance Image (MRI) with non-uniform random sampling, a variation-based reconstruction approach was used for resolution enhancement of volumetric images, with the purpose to preserve important edges containing anatomical information [32]. Moreover, the randomness of the detected photons in positron emission tomography (PET) requires TV regularization to suppress noise while maintaining sharp edges without oscillations [33].…”